摘要
针对传统ORB算法阈值选取固定,存在误提取、误匹配,无法满足不同图像特征点的准确提取和匹配的问题,提出了一种改进的ORB特征点提取与匹配方法。首先设定局部自适应阈值;然后通过像素分类,设计自适应阈值选取准则,达到ORB特征点的精准提取;最后在改进ORB特征点基础上通过PROSAC算法完成对特征点的匹配。实验结果表明,改进后的方法对亮度变化具有较强的适应能力,计算速度和提取精度得到了提升。匹配总时间降低,误匹配点对数量较少,正确匹配率较高,具有良好的准确性和实时性。利用匹配阶段得到的特征点进行跟踪时得到的RMSE误差较小,表明匹配精度得到了较大提升。和其他方法相比,具有更好的环境适应能力和应用价值。
The fixed threshold selection of traditional ORB algorithm results in many false extractions and mismatches,which cannot meet the requirements of accurate extraction and matching of different image feature points.To solve this problem,an improved ORB feature point extraction and matching method was proposed.Firstly,the local adaptive threshold was set up.Then,an adaptive threshold selection criterion was designed by classifying the pixels,and thus the precise extraction of ORB feature points was achieved.Finally,the PROSAC algorithm was used to complete the matching of feature points based on the improved ORB feature points.The experimental results indicate that the improved method has a high adaptability to variations in brightness,and both the calculation speed and extraction accuracy are greatly improved.The total matching time is reduced,the number of mismatches is less,and the accurate matching rate is increased,which indicates that this improved method is characterized with accuracy and real-time performance.In addition,the RMSE error obtained by tracking the feature points acquired at the matching stage is small,which demonstrates a significant improvement in matching accuracy.Compared with other existing methods,this method has better environmental adaptive capacity and application value.
作者
杨弘凡
李航
陈凯阳
李嘉琪
王晓菲
YANG Hong-fan;LI Hang;CHEN Kai-yang;LI Jia-qi;WANG Xiao-fei(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang Henan 471003,China)
出处
《图学学报》
CSCD
北大核心
2020年第4期548-555,共8页
Journal of Graphics
基金
国家重点研发计划重点专项(2018YFB200502)
河南省科技攻关项目(182102110420)。
关键词
特征点提取
局部自适应阈值
重复率
特征点对匹配
跟踪
feature point extraction
local adaptive threshold
repetition rate
point pairs matching
tracking
作者简介
第一作者:杨弘凡(1994-),男,河南洛阳人,硕士研究生。主要研究方向为计算机视觉,E-mail:hongfan1017@sina.com。
